Introduces randomly infused advice (RIA) to analyze existing online algorithms with partial reliable advice infused into random bits, establishing improved competitive-ratio upper bounds (and often tight lower bounds) for paging, uniform metrical task systems, and online set cover as alpha increases
Near-optimal bounds for online caching with machine learned advice
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Online Algorithms with Randomly Infused Advice
Introduces randomly infused advice (RIA) to analyze existing online algorithms with partial reliable advice infused into random bits, establishing improved competitive-ratio upper bounds (and often tight lower bounds) for paging, uniform metrical task systems, and online set cover as alpha increases